A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash
| Autor: | Furqan Farooq, Slawomir Czarnecki, Pawel Niewiadomski, Fahid Aslam, Hisham Alabduljabbar, Krzysztof Adam Ostrowski, Klaudia Śliwa-Wieczorek, Tomasz Nowobilski, Seweryn Malazdrewicz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
self-compacting concrete
fly ash machine learning artificial neural network gene engineering programming Technology Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 |
| Zdroj: | Materials, Vol 14, Iss 17, p 4934 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1996-1944 |
| DOI: | 10.3390/ma14174934 |
| Popis: | Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect. |
| Databáze: | Directory of Open Access Journals |
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